Selection of regions of interest in an image sequence is a typical
prerequisite step for estimation of time-activity curves in dynamic positron
emission tomography (PET). This procedure is done manually by a human
operator and therefore suffers from subjective errors. Another such problem
is to estimate the input function. It can be measured from arterial blood or
it can be searched for a vascular structure on the images which is hard to be
done, unreliable, and often impossible. In this study, we focus on blind
source separation methods with no needs of manual interaction. Recently, we
developed sparse blind source separation and deconvolution (S-BSS-vecDC)
method for separation of original sources from dynamic medical data based on
probability modeling and Variational Bayes approximation methodology. In this
paper, we extend this method and we apply the methods on dynamic brain PET
data and application and comparison of derived algorithms with those of
similar assumptions are given. The S-BSS-vecDC algorithm is publicly
available for download.
Estimation of input function from dynamic PET brain data using Bayesian blind source separation
